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    Two-Stage Optimal Dispatch for Integrated Energy System with Oxy-Combustion Based on Multi-Energy Flexibility Constraints
    PENG Chuxuan, BIAN Xiaoyan, JIN Haixiang, LIN Shunfu, XU Bo, ZHAO Jian
    Journal of Shanghai Jiao Tong University    2025, 59 (9): 1281-1291.   DOI: 10.16183/j.cnki.jsjtu.2023.487
    Abstract1363)   HTML5)    PDF(pc) (2004KB)(5460)       Save

    As one of the most promising carbon capture technologies for coal-fired power plants, oxy-fuel combustion provides a new solution for improving the flexibility of the integrated energy system (IES) and reducing carbon emissions. In this paper, a two-stage optimal dispatch strategy for the integrated energy system with oxy-fuel combustion units considering the constraints of multi-energy flexibility is proposed based on the intergration of oxy-fuel combustion technology and the optimal operation of the integrated energy system. First, a model of integrated energy system with oxy-fuel combustion (Oxy-IES) is established. Then, a matrix model of multi-energy flexibility constraints for Oxy-IES is proposed to reveal the supply and demand relationship of flexibility within the system. Finally, a two-stage optimization dispatch strategy for Oxy-IES is constructed, in which the output of each unit is optimized to minimize the daily operating cost of carbon trading in the day-ahead stage, while the rapid variable load capacity of the oxy-fuel combustion unit improves the flexibility of the system in the intraday stage. The simulation results of Oxy-IES show that the proposed strategy can improve the flexibility and economy performance of the IES while reducing carbon emissions.

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    Renewable Energy Consumption Strategies of Power System Integrated with Electric Vehicle Clusters Based on Load Alignment and Deep Reinforcement Learning
    LIU Yanhang, QIAO Ruyu, LIANG Nan, CHEN Yu, YU Kai, WU Hanxiao
    Journal of Shanghai Jiao Tong University    2025, 59 (10): 1464-1475.   DOI: 10.16183/j.cnki.jsjtu.2023.529
    Abstract1463)   HTML3)    PDF(pc) (3345KB)(3663)       Save

    As China accelerates the construction of power systems with renewable energy as the mainstay, the large-scale integration of renewables has led to prominent issues such as wind and light curtailment. To improve the utilization of new energy consumption in power systems, this paper proposes a novel renewable energy consumption method based on load alignment and deep reinforcement learning. First, it proposes a node load line formation model based on linearized power flow calculations, which can guide adjustable loads to shift the electricity consumption period, thereby promoting the improvement of new energy consumption. Unlike the direct current (DC) power flow model, the proposed alternating current (AC) model accounts for voltage constraints and other related constraints of the power system. Compared with other AC power flow models, this model linearizes all nonlinear constraints and has lower computational costs. Then, this paper constructs a market framework for load alignment mechanism. The framework involves three main entities: independent system operators, regional power grid sellers, and electric vehicle adjustable load aggregators. It also explores the solution for load alignment incentive prices using electric vehicle clusters as adjustable loads. As the solution of the load benchmark incentive price involves a master-slave game between three entities, conventional mathematical analysis methods face high complexity. Therefore, it employs deep reinforcement learning algorithm to solve the problem. The deep reinforcement learning algorithm takes the marginal electricity price of each node as state space, the load benchmark incentive price as action space, and the cost of regional power grid sellers as feedback. The agent can find the load line incentive price that maximizes the benefits of regional power grid sellers after continuous training. Finally, the example analysis shows that the load alignment mechanism not only effectively promotes the improvement of new energy consumption level, but also enhances the interests of independent system operators, regional power grid sellers, and electric vehicle aggregators. The results further confirm that the deep reinforcement learning algorithm maximizes the benefits of regional power grid sellers.

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    A Short-Term Carbon Emission Accounting Method for Power Industry Using Electricity Data Based on a Combined Model of CNN and LightGBM
    ZENG Jincan, HE Gengsheng, LI Yaowang, DU Ershun, ZHANG Ning, ZHU Haojun
    Journal of Shanghai Jiao Tong University    2025, 59 (6): 746-757.   DOI: 10.16183/j.cnki.jsjtu.2023.382
    Abstract2372)   HTML13)    PDF(pc) (6089KB)(3475)       Save

    The electric power industry plays a pivotal role in carbon emission control. Accurate and real-time accounting of carbon emissions in the power industry is essential for supporting the carbon reduction of the power industry. At present, the measurement of carbon emissions in the power industry relies mainly on direct measurement or the accounting methods, which often struggles to balance low measurement costs with real-time accuracy. Therefore, in this paper, the robust power data infrastructure in the power industry is leveraged and the correlation between electricity consumption and carbon emissions is explored to propose a short-term electricity-to-carbon method using machine learning methods based on historical data of electricity. This method utilizes convolutional neural networks (CNNs) for feature extraction, and light gradient boosting machine (LightGBM) for carbon emission estimation based on extracted features. Moreover, K-fold cross-validation is used in model training, with parameter optimization using grid search to enhance the generalization capability and robustness of the model. To validate the proposed method, it is compared with other machine learning models under the same data segmentation condition for daily and hourly data sets. The results indicate that the proposed model outperforms other models in both performance evaluation and the consistency between estimated and target values.

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    Detection of Roadside Vehicle Parking Violations Under Random Horizontal Camera Condition
    ZHAN Zehui, ZHONG Ming’en, YUAN Bingan, TAN Jiawei, FAN Kang
    Journal of Shanghai Jiao Tong University    2025, 59 (10): 1568-1580.   DOI: 10.16183/j.cnki.jsjtu.2023.578
    Abstract552)   HTML5)    PDF(pc) (46166KB)(2724)       Save

    Investigation and punishment of vehicle parking violations is important in urban traffic management. Considering the time-consuming and labor-intensive nature of manual law enforcement, as well as the limited scope of fixed camera monitoring and detecting, exploring more flexible and efficient automatic detection methods has a great practical significance. Thus, a cruise detection technology is proposed, which is suitable for mobile carriers requiring no stopping and can be completed in a single pass. First, a vehicle parking violation image dataset named XMUT-VPI is collected and constructed under the conditions of approximate horizontal views and random shooting angles, laying a data foundation for the research. Then, a multitask parking network (MTPN) is constructed as an encoder to extract the key element information required for stop violation judgment. With the aid of the self-designed deformable large kernel feature aggregation module (DLKA-C2f) and cross-task interaction attention mechanism (CTIAM), a highest average detection accuracy of 90.3%, a minimum average positioning error of 4.4%, and a suboptimal average segmentation intersection ratio accuracy of 78.5% are achieved. Finally, an efficient decoder is designed to further extract the skeleton features of the parking space line and fit the visible area of the main parking space, which helps match the target vehicle and analyzes the positional condition between its tire ground-touching points and the main parking space. In addition, a judgment principle is provided for three typical behaviors of illegal parking, improper parking, and standardized parking. Experimental results show that the algorithm attains a comprehensive accuracy rate of 98.1% for vehicle parking violation detections across diverse complex interference scenarios, which outperforms existing mainstream methods and can provide technical supports for fully automate road cruise management of parking violatic.

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    Transient Modeling and Characteristic Comparative Analysis of Grid-Forming VSC with and Without Current Control
    REN Xiancheng, LI Shangzhi, LI Yingbiao, HU Jiabing, XU Taishan, BAO Yanhong, WU Feng
    Journal of Shanghai Jiao Tong University    2025, 59 (7): 971-982.   DOI: 10.16183/j.cnki.jsjtu.2023.416
    Abstract1526)   HTML13)    PDF(pc) (2357KB)(2670)       Save

    As the support capacity of renewable energy generation equipment for the power grid needs enhancement, grid-forming control has attracted extensive attention, among which the virtual synchronous generator (VSG) has emerged as a key research frontier and is already being applied in engineering demonstration. Voltage source converter (VSC) with VSG as the synchronization link can be classified into voltage and current dual loop control and direct voltage control according to whether there is a current control loop in the structure. The difference in the two control structures has a significant impact on the transient characteristics of VSC. To study the difference between transient characteristics of two kinds of VSCs, the transient models are developed based on the “power excitation-internal voltage response” model, and the formation mechanism of internal voltage and transient characteristics are comparatively analyzed. Since the VSG simulates the operation characteristics of the synchronous machine, the equivalent inertia and equivalent damping of the VSC are analytically obtained at the electromechanical scale, and their transient behaviors are compared. It is found that the equivalent inertia and damping of a VSC with direct voltage control remain constant, while those of a VSC with voltage and current dual loop control exhibit time-varying characteristics and are numerically smaller than of the direct voltage control system. Finally, the validity of the theoretical analysis is confirmed by electromagnetic transient simulation.

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    An Evolutionary Game Approach to Incentive Mechanism of Vehicle-to-Grid
    PAN Yi, WANG Mingshen, MIAO Huiyu, YUAN Xiaodong, HAN Huachun
    Journal of Shanghai Jiao Tong University    2025, 59 (11): 1637-1646.   DOI: 10.16183/j.cnki.jsjtu.2023.603
    Abstract1639)   HTML6)    PDF(pc) (4079KB)(2380)       Save

    Electric vehicles (EVs) can provide significant support for the flexible operation of power systems, in which vehicle-to-grid (V2G) mode is an important way for EVs to participate in the frequency and voltage regulation of power grids. However, the commercialization of V2G has experienced slow progress to date, and the lack of an effective market operation mechanism makes it difficult for large-scale EVs to participate in the ancillary services of the grid. Therefore, a novel evolutionary game model is proposed with the participation of the electricity regulatory commission, power grid company, and EVs and the impact of the strategic choices of the three parties on the operation of the V2G market is explored to identify the subsidy and pricing mechanisms for the government to facilitate the long-term evolution of the V2G. First, replicator dynamic equations for the game are established to investigate the stability of multiple strategy equilibrium points in the three-party evolutionary game. Then, the Lyapunov stability theory is employed to analyze the stability of these equilibrium points and to determine the subsidy amount to promote V2G development. Next, a simulation analysis is conducted on the actual electricity price data from Shanghai in China, which quantitatively identified the government subsidy coefficient range and electricity price range to incentivize EV participation in the V2G model. The simulation results provide theoretical support for the electricity regulatory commission and power grid company in formulating subsidy and pricing strategies.

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    Cascade Sliding Mode Decoupling Control of Coupled Inductor Single-Input Dual-Output Buck Converter Based on Super-Twisting Extend State Observer
    HUANG JinFeng, ZHANG Qian
    Journal of Shanghai Jiao Tong University    2025, 59 (5): 592-604.   DOI: 10.16183/j.cnki.jsjtu.2023.349
    Abstract1604)   HTML3)    PDF(pc) (4220KB)(2244)       Save

    To address the coupling effect between the output branches of the coupled inductor single-input dual-output (CI-SIDO) Buck converter, which leads to the cross-influence and thus affects the dynamic performance of the system, a cascaded sliding mode decoupling control strategy based on the super-twisting extend state observer (ST-ESO) is proposed. First, a state-space averaging model of the CI-SIDO Buck converter is established. Then, the coupling terms, internal perturbations, and unmodeled parts in the inner and outer loops of the converter are estimated by using the ST-ESO with a fast-convergence property, which are regarded as the total perturbations in the inner and outer loops. Next, the total perturbation in the inner and outer loops is compensated by using a super-twisting sliding mode controller to achieve the decoupling of the system and ensure the robustness of the system and the stability of the output voltage. Furthermore, the stability of the super-twisting extend state observer and super-twisting sliding mode controller is analyzed using the Lyapunov theory, providing theoretical verification of the feasibility of the control strategy. Finally, the proposed control strategy is experimentally validated on the experimental platform. The results show that the proposed control strategy realizes the decoupling of the system, suppresses the cross-influence and improves the dynamic performance of the system.

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    Online Steady-State Scheduling of New Power Systems Based on Hierarchical Reinforcement Learning
    ZHAO Yingying, QIU Yue, ZHU Tianchen, LI Fan, SU Yun, TAI Zhenying, SUN Qingyun, FAN Hang
    Journal of Shanghai Jiao Tong University    2025, 59 (3): 400-412.   DOI: 10.16183/j.cnki.jsjtu.2023.344
    Abstract1929)   HTML9)    PDF(pc) (4192KB)(2159)       Save

    With the construction of new power systems, the stochasticity of high-proportion renewable energy significantly increases the uncertainty in the operation of the power grid, posing severe challenges to its safe, stable, and economically efficient operation. Data-driven artificial intelligence methods, such as deep reinforcement learning, are becoming increasingly important for regulating and assisting decision-making in the power grid in the new power system. However, current online scheduling algorithms based on deep reinforcement learning still face challenges in modeling the high-dimensional decision space and optimizing scheduling strategies, resulting in low model search efficiency and slow convergence. Therefore, a novel online steady-state scheduling method is proposed for the new power system based on hierarchical reinforcement learning, which reduces the decision space by adaptively selecting key nodes for adjustment. In addition, a state context-aware module based on gated recurrent units is introduced to model the high-dimensional environmental state, and a model with the optimization objectives of comprehensive operating costs, energy consumption, and over-limit conditions is constructed considering various operational constraints. The effectiveness of the proposed algorithm is thoroughly validated through experiments on three standard test cases, including IEEE-118, L2RPN-WCCI-2022, and SG-126.

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    The Inter-disciplinarity of International and Regional Studies: With Views on the Integration of Disciplines of Foreign Languages and World History
    LI La, WANG Yuming
    Contemporary Foreign Languages Studies    2025, 25 (1): 191-201.   DOI: 10.3969/j.issn.1674-8921.2025.01.018
    Abstract418)   HTML2)    PDF(pc) (1425KB)(2092)       Save

    On September 14th, 2022, the Academic Degrees Committee of the State Council and the Ministry of Education issued the Catalogue of Graduate Education Subjects and Specialties (2022) and the Management Measures of the Catalogue of Graduate Education Subjects and Specialties, which officially declared International and Regional Studies to be established as a first-tier discipline under the category of interdisciplinary disciplines. It proves the necessity of integrating this discipline with other related disciplines. The interdisciplinary and multidisciplinary nature of area studies is mainly embodied in its definition and disciplinary distribution of the researchers involved, which can be concluded by sorting out the definitions made by different scholars at home and abroad, and analyzing the distribution of overseas research disciplines in the projects supported by the National Social Science Fund and the distribution of the humanities and social science projects funded by the Ministry of Education. According to the results, it can also be fourd that two disciplines of Foreign Languages and World History make major contributions to International and Regional Studies. Languages serve as indispensable tools, while the discipline of World History uses languages to study the history of various countries and regions in depth. Thus, it will be highly beneficial for the development of International and Regional studies to combine Foreign Languages Discipline with the discipline of World History through multiple degrees conferring, faculty sharing and building academic communities.

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    Analysis and interpretation of the 2022 Global Cancer Statistics Report: cancer burden and epidemiological trends in China and the world
    WU Qi, FAN Bonan, LI Yan
    Journal of Diagnostics Concepts & Practice    2025, 24 (02): 135-145.   DOI: 10.16150/j.1671-2870.2025.02.004
    Abstract2224)   HTML86)    PDF(pc) (1203KB)(2045)       Save

    In February 2024, the International Agency for Research on Cancer (IARC) released the 2022 Global Cancer Statistics Report. In 2022, there were nearly 20 million new cancer cases and 9.7 million deaths. The report provides statistics on the incidence and mortality of 36 different types of cancer in 185 countries around the world, analyzing geographic, gender-based, and Human Development Index (HDI)-related differences. It also predicts the global burden of cancer disease by 2050. Demographic forecasts suggest that by 2050, the number of new cancer cases worldwide is expected to reach 35 million annually-an increase of 77% compared to 2022. Geographically, cancer incidence and mortality rates show significant regional disparities. In 2022, nearly half (49.2%) of the world's new cases and the majority (56.1%) of cancer deaths occurred in Asia. In terms of gender distribution, the overall cancer incidence and mortality rate among females were lower than those among males in 2022. With respect to HDI, the risk of developing cancer increases with higher HDI levels. In 2022, the top 5 newly diagnosed cancer cases worldwide are lung cancer, female breast cancer cancer, colorectal cancer, prostate cancer, gastric cancer in turn. There were nearly 2.5 million new lung cancer cases and over 1.8 million related deaths. Breast cancer in women accounted for 2.3 million new cases and nearly 670 000 deaths. Colorectal cancer, including anal cancer, had more than 1.9 million new cases and over 900 000 deaths. Prostate cancer recorded 1.5 million new cases and nearly 400 000 deaths. There were nearly 970 000 newly-diagnosed cases of gastric cancer and 660 000 related deaths. In China in 2022, lung cancer still ranks first in the cancer incidence spectrum in China, accounting for 22.0% of the total new cases of cancer in China. This proportion has further increased compared to 2018 data (17.9%), followed by colorectal cancer (10.7%), thyroid cancer (9.7%), liver cancer (7.6%), and gastric cancer (7.4%), which account for more than half of the total new cases in China (57.4%). This paper reviews the data sources and statistical methods used in the report, interprets the epidemiological trends of major cancer types, and analyzes the incidence and burden of major cancers prevalent in China, provi-ding an overview of their disease burden and epidemiological trends.

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    Capacity Planning and Operational Optimization for Low-Carbon Data Center Integrated Energy System Considering Exergy Efficiency
    LIN Jiayu, HAN Juntao, WANG Yongzhen, HAN Kai, HAN Yibo, LI Jian
    Journal of Shanghai Jiao Tong University    2025, 59 (9): 1327-1337.   DOI: 10.16183/j.cnki.jsjtu.2023.528
    Abstract1715)   HTML6)    PDF(pc) (3820KB)(2029)       Save

    With the rapid development of the digital economy, the energy consumption and carbon emissions of data centers (DCs) have significantly increased. In recent years, the construction of data center integrated energy systems (DC-IES) has emerged as one of the critical trends in energy conservation and emission reduction for DCs under the global net-zero emission initiative. To support the planning and construction of low-carbon DC-IES, this paper proposes a multi-objective optimization model for capacity allocation and operational planning of DC-IES, integrating energy and economic considerations with a focus on low-carbon performance. Based on the “quality” analysis method of exergy from the second law of thermodynamics, the model proposed comprehensively accounts for the dynamic exergy efficient characteristics of energy conversion devices under varying load conditions, revealing the energy flow distribution characteristics of DC-IES under different objectives. The computational results indicate that compared with the optimization scheme assuming constant equipment efficiency, the scheme considering dynamic equipment efficiency reduces energy loss rate, economic cost, and carbon emissions by 2.6%, 1.9%, and 4.8%, respectively, demonstrating clear advantages. Moreover, compared with the economically optimal scheme, the multi-objective optimization scheme significantly reduces carbon emissions and energy loss rate of the DC-IES by 22.72% and 20.73%, respectively. Furthermore, compared to the scheme scenarios with the minimum exergy loss rate and lowest carbon emissions, the multi-objective optimization scheme reduces economic costs by 54.54% and 60.78%, respectively. Compared with the scheme relying solely on grid electricity supply, the multi-objective optimization scheme that regards the DC as an integrated energy system can reduce carbon emissions by 40.97%.

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    Reactive Power-Voltage Droop Gain Online Tuning Method of Photovoltaic Inverters for Improvement of Stable Output Power Capability in Weak Grids
    WANG Yuyang, ZHANG Chen, ZHANG Yu, WANG Yiming, XU Po, CAI Xu
    Journal of Shanghai Jiao Tong University    2025, 59 (6): 845-856.   DOI: 10.16183/j.cnki.jsjtu.2023.353
    Abstract2007)   HTML7)    PDF(pc) (7800KB)(1985)       Save

    The active power output capability and small signal stability in weak grids are key factors that limit stable photovoltaic (PV) power generation. To improve stably generating PV power in weak grids, an adaptive control method for PV inverters based on online tuning of the reactive power-voltage (Q-V) droop gain is proposed. First, to ensure active power output capability in weak grids, a “first optimization” method for the Q-V droop gain is proposed, considering voltage and current constraints. Then, to address stability constraints in weak grids, impedance modeling and stability analysis of the PV inverter system are conducted. A mapping relationship between the “parameter-weakest pole” is established with the weakest pole of the closed-loop system as a stability constraint based on the artificial neural network. A “second adjustment” method for the Q-V droop gain is developed at stably generating active power. Combined with the extended Kalman-filter-based grid impedance estimation, the proposed Q-V droop gain adaptive tuning method is realized. The effectiveness of the proposed adaptive control method is validated on the Modeling Tech real-time simulation platform.

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    Deep Learning and Knowledge Infusion:An Inquiry into Path Construction for a Translation-Technology Teaching Model
    DAI Guangrong, SHEN Siyi, HUANG Dongliang
    Contemporary Foreign Languages Studies    2025, 25 (1): 125-138.   DOI: 10.3969/j.issn.1674-8921.2025.01.012
    Abstract525)   HTML4)    PDF(pc) (1420KB)(1943)       Save

    In the era of Language Intelligence, translation pedagogy ushers into a more diverse and complex landscape, knowledge within this system broadens deeper than ever, but students’ learning didn’t go to depth with the development of technology. The shallow learning-based education model is no longer suitable for the current translation pedagogy and its knowledge translation production and spreading. This paper aims to explore innovative pathways to address the predicament in talent cultivation. With Transknowletology as its core, this paper elucidates the concept, content and scope of deep learning and puts forward the model for translation technology education combined with deep learning and knowledge system. Lastly, this paper attempts to construct an efficient and scientific path of enhancing translation technological thinking skills, in a bid to enhance the knowledge translation development and acceptance.

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    On the Dialectics of Constructing the Theory of Transknowletology
    LIU Junping
    Contemporary Foreign Languages Studies    2025, 25 (1): 96-111.   DOI: 10.3969/j.issn.1674-8921.2025.01.010
    Abstract724)   HTML9)    PDF(pc) (1285KB)(1858)       Save

    Professor Yang Feng, the founder of Transknowletology, put forward the proposition that “The fusion of knowledge in science, sociology and humanities will solve the puzzle and problem of classification anxiety for the translation discipline for the time to come. Translatology armed with the knowledge of natural sciences, social sciences and humanities will inevitably become a meta discipline or supra subject”(Yang Feng 2021:2). Proceeding from the insightful statement, we can observe that Transknowletology intends to integrate the resources and refine its key concepts and reformulate its epistemic framework from the above three disciplines based on complex system to construct the meta-knowledge of translatology. Thus, Transknowletology as general translatology will necessarily touch upon the knowledge and ideas of translatology, philosophy, sociology, cognition, hermeneutics. Through analysis and synthesis, construction and refining, it will become a meta-discipline for translation studies embodying both the logical facts and ethic values.If it intends to reach such a goal, Transknowletology must include epistemic concepts, paradigms and epistemic reintegration and reclassification on its road map so as to reformulate its model. On the epistemic structure, it should proceed from the onotology, methodology and teleology of epistemology, attach importance to the dialectics of methodology, integrate East-West epistemology,reorganize the internal and external knowledge of translatology, give weight to humanistic, aesthetic and ethic knowledge. Thus formulated, it will depart from the binary separation of knowledge to the existential and life world experiences. Consequently, it will lessen the tensions between the scientific rationality and humanistic reason. Based on the “ method of harmonious balance of Transknowletology ” and the logic of dialogue, the newly formed branch of learning will perfect its methodology of restructuring its knowledge base and will eventually deal with the ultimate concern of “what is a translator” so as to actualize the “Epistemic Turn” in contemporary Translation Studies.

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    Human-Machine Collaborative Intelligent Writings: Its Evolution and Implications
    LI Jingjie, CHEN Qiuyan
    Contemporary Foreign Languages Studies    2025, 25 (1): 73-83.   DOI: 10.3969/j.issn.1674-8921.2025.01.008
    Abstract1057)   HTML30)    PDF(pc) (1263KB)(1844)       Save

    Artificially intelligent writing has become an irreversible trend. This article, from the perspective of human-machine collaboration, provides an assessment and comparison of artificial intelligence writing systems in three stages: the emergence stage of AI writing, the stage of diversified exploration, and the stage of comprehensive development. It analyzes the characteristics and development trends of human-machine collaboration in intelligent writing systems at different stages. Building upon this analysis, it discusses the progress, issues, and implications of human-machine collaborative intelligent writing systems in academic English writing and teaching in terms of the integration of writing assistance and revision assistance, dialogue-based interaction, comprehensive coverage of the writing process, and Man-Computer symbiosis.

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    Control Strategy for Improving Active Frequency Support Capability of Offshore Wind Farm
    LI Yibo, ZHOU Qian, ZHU Dandan, JIANG Yafeng, WU Qiuwei, CHEN Jian
    Journal of Shanghai Jiao Tong University    2025, 59 (10): 1442-1450.   DOI: 10.16183/j.cnki.jsjtu.2023.581
    Abstract1785)   HTML5)    PDF(pc) (1522KB)(1784)       Save

    In low frequency alternating current (AC) transmission systems, offshore wind farm is unable to respond to changes in onshore grid frequency in a timely manner due to frequency decoupling and signal transmission delays between the offshore wind power system and the onshore AC system. To address this issue, a control strategy is proposed to improve the active frequency support capability of offshore wind farms by combining the system inertia. In terms of frequency signaling, an additional frequency sag controller is designed based on the V/f control strategy of the low-frequency-side structure network of modular multilevel matrix converter (M3C), combining with the system inertia. The frequency coupling link between the M3C net side and the low-frequency side is established to realize the real-time transmission of frequency information between the two sides. In terms of frequency support, when the system is disturbed to generate frequency deviation, the offshore wind turbine can adjust the power command value through additional droop control, thereby providing frequency support for the system. Finally, the effectiveness of the proposed coordinated control strategy is verified in MATLAB/Simulink by the simulation of load change and three-phase AC short circuit fault.

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    Foreign Language Teaching and Professional Development of Foreign Language Teachers in the Era of AI: Challenges, Identity Tensions and Solutions
    XU Yueting, GU Yue
    Contemporary Foreign Languages Studies    2025, 25 (1): 60-72.   DOI: 10.3969/j.issn.1674-8921.2025.01.007
    Abstract1517)   HTML15)    PDF(pc) (1301KB)(1643)       Save

    Artificial intelligence (AI) represented by ChatGPT can not only promote learners to learn foreign languages but also improve teachers’ work efficiency and reduce their work burden. However, the educational revolution triggered by ChatGPT brings challenges to foreign language teaching and results in identity tensions to foreign language teachers. By reviewing research on the application of AI in the field of foreign language teaching and language teacher identity, this paper analyses challenges of foreign language teaching and teachers, identity tensions and solutions, and puts forward corresponding suggestions from three aspects, including teachers, schools, and government. This paper not only provides theoretical support and practical guidance for the implementation of AI-assisted teaching in the field of foreign language education but also has enlightenment for other subjects in dealing with teacher identity tensions and exploring the path of deep integration of AI and education.

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    A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning
    PAN Meiqi, HE Xing
    Journal of Shanghai Jiao Tong University    2025, 59 (5): 561-568.   DOI: 10.16183/j.cnki.jsjtu.2023.375
    Abstract1920)   HTML18)    PDF(pc) (1123KB)(1467)       Save

    In engineering practice, wind turbine fault diagnosis encounters situations where the fault category in the training data is different from the actual one. To diagnose unknown wind turbine faults, it is necessary to transfer the fault feature information learned during training to the unknown fault category. Unlike traditional methods that directly establish mapping between fault samples and fault categories, a zero-shot learning (ZSL) method for wind turbine fault diagnosis based on fault attributes is proposed to enable fault feature migration. A fault attribute matrix is established by describing the attributes of each fault, which is embedded into the fault sample space and fault category space. Then, a fault attribute learner is developed based on convolutional neural network (CNN), and a fault classifier is established based on Euclidean distance, forming the diagnosis process where fault attributes are predicted from fault samples and then classified. Finally, the effectiveness and superiority of the proposed fault diagnosis method are validated by comparing it with other zero-shot learning methods.

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    Fatigue Strength Analysis of Ship’s Welded Structures Based on Method of Notch Stress
    ZHEN Chunbo, LIU Shihao, ZHANG Aifeng, XING Shizhu, ZHANG Runze
    Journal of Shanghai Jiao Tong University    2025, 59 (4): 458-465.   DOI: 10.16183/j.cnki.jsjtu.2023.307
    Abstract1572)   HTML8)    PDF(pc) (8884KB)(1464)       Save

    To address the fatigue strength problem of ship structures, the notch stress method is applied to typical structural joints focusing on weld toe and weld root. First, the basic principles of notch stress method and the notch fatigue analysis process of ship structure are introduced. Then, six typical joint types are set up for the double bottom structure of a product oil tanker. The local notch stress analysis of the fatigue hot spot is conducted using the finite element sub-modeling technology, allowing the determination of the notch stress concentration factor at the weld toe and weld root. Finally, based on the harmonised common structural rules (HCSR), the fatigue load and load condition of the ship structure are calculated, and the fatigue analysis of six typical joint types is performed. The results show that under the same load conditions, the notch stress concentration factor at the weld toe of each hot spot is smaller than that at the weld root, and the joint type 3 has the lowest fatigue damage value among the joint types, which suggests this type can improve the fatigue resistance of the hull.

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    An Improved Multi-Objective Evolutionary Algorithm for Grid Map Path Planning
    DONG Dejin, WANG Changcheng, CAI Yunze
    Journal of Shanghai Jiao Tong University    2025, 59 (10): 1558-1567.   DOI: 10.16183/j.cnki.jsjtu.2024.032
    Abstract500)   HTML13)    PDF(pc) (4493KB)(1437)       Save

    Multi-objective path planning on large-scale grid maps is characterized by a large number of nodes and multiple targets. Existing algorithms struggle to balance the speed and quality of solving the Pareto front (PF). Therefore, studying efficient optimization algorithms based on the PF has certain theoretical significance. First, a weighted graph modeling method based on cost vector is proposed, and optimization algorithms for solving large-scale problems are studied accordingly, which significantly saves time and costs compared with graph search algorithms. Then, to address the issue of low quality of the PF solutions, an improved multi-objective evolutionary algorithm is proposed, which includes a new initialization strategy. Individual and environment selection strategies are designed based on the concepts of angle and shift-based density. These improvements take both population diversity and convergence into account, thereby improving the solving efficiency. Finally, comparative simulation experiments are conducted to verify the effectiveness of the improved algorithm.

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    Optimization of Installed Wind Power Capacity Considering Dynamic Frequency Constraints and Multiple Uncertainties
    YE Jing, HE Jiehui, ZHANG Lei, CAI Junwen, LIN Yuqi, XIE Jihao
    Journal of Shanghai Jiao Tong University    2025, 59 (9): 1292-1303.   DOI: 10.16183/j.cnki.jsjtu.2023.474
    Abstract1717)   HTML1)    PDF(pc) (1708KB)(1391)       Save

    As the installed capacity of wind power continues to increase, the frequency security of new power system becomes increasingly significant. To guarantee the frequency security of the system, improve the frequency regulation capability of the system, and determine an optimal wind power installed capacity, a wind power installed capacity optimization model considering dynamic frequency constraints as well as load-side inertia is proposed. First, the dynamic frequency response model with load-side inertia is derived. Then, fuzzy opportunity constraints are introduced considering the uncertainty of wind power, load, and load-side inertia. Taking into account the dynamic frequency constraints, the model incorporates multiple uncertainty fuzzy opportunity constraints, in which the uncertainty constraints are clearly converted into equivalence classes. Finally, to address the dynamic frequency-constrained nonlinear characteristic, the optimization problem is partitioned into a main problem and sub-problems for solution. The validity and feasibility of the proposed model are validated by using an improved 10-machine system.

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    Overview of the Development and Design of Dynamic Cable for Floating Offshore Wind Power in China
    LIN Zeyin, WANG Yibing, LI Dongsheng
    Ocean Engineering Equipment and Technology    2024, 11 (4): 41-46.   DOI: 10.12087/oeet.2095-7297.2024.04.07
    Abstract928)      PDF(pc) (2023KB)(1380)       Save
    In recent years, the global offshore wind power has developed rapidly. As nearshore resources become increasingly scarce, offshore wind power has gradually moved to the deeper sea. To accommodate the dynamic characteristics of floating wind turbines, floating platforms must be supported by dynamic cables. This is a comprehensive cable of transmitting electricity and control signals, with constantly changing position and force status, which can withstand the combined effect of wind, wave, current and other natural environment, to ensure the effective transmission of power and monitoring signals. Compared with foreign industries, Chinas dynamic cable industry started later, with the harsh working conditions and the lack of relevant technology. All these factors restrict the progress of the dynamic cable industry. This paper will provide a detailed introduction to the basic principles, current development status, key technologies, and challenges faced by floating wind turbine dynamic cables, summarizing the latest industry information and technology to support the development of Chinas dynamic cable industry.
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    Multi-Objective Optimization Design of Micro-Site Selection of Complex Terrain Wind Farms Assisted by Proxy Model
    LIU Jiahui, WANG Cong, ZHANG Hongli, MA Ping, LI Xinkai, DONG Yingchao
    Journal of Shanghai Jiao Tong University    2025, 59 (9): 1315-1326.   DOI: 10.16183/j.cnki.jsjtu.2023.486
    Abstract2002)   HTML9)    PDF(pc) (5456KB)(1329)       Save

    To tackle the challenges of high difficulty and time-consuming micro-site optimization of wind farms in complex terrains, a multi-objective optimization method for micro-site selection is proposed, assisted by proxy model. First, considering the geographical features of complex terrains with significent undulations, the ruggedness index is calculated and the ground flatness is numerically quantified, constraining the points with excessive ruggedness. Then, a mathematical model for three-dimensional windy downward wake superposition calculation of power generation is established, a three-dimensional terrain collector line topology optimization agent model is constructed, and the prediction accuracy of the proxy model is verified, demonstrating the ability to replace numerous calculations in collector line topology optimization and effectively improving the computing efficiency. Finally, taking a real complex terrain wind farm in Xinjiang Uygur Autonomous Region, China as an example, multi-objective micro-site selection of complex terrain wind farm is realized, and the results are compared with those obtained through the single-objective optimization. The simulation results show that the multi-objective discrete state transfer algorithm assisted by the proxy model can reduce the total cable laying length, decrease the construction costs, and provide more feasible layout schemes while optimizing the annual power generation.

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    Modeling of Cloud-Edge Collaborated Electricity Market Considering Flexible Ramping Products Provided by VPPs
    PENG Chaoyi, CHEN Wenzhe, XU Suyue, LI Jianshe, ZHOU Huafeng, GU Huijie, NIE Yongquan, SUN Haishun
    Journal of Shanghai Jiao Tong University    2025, 59 (2): 186-199.   DOI: 10.16183/j.cnki.jsjtu.2023.240
    Abstract622)   HTML6)    PDF(pc) (3279KB)(1287)       Save

    Due to its load time shifting and power regulation capabilities, virtual power plants (VPPs) have the potential to participate in the electricity market and provide flexible ramping products (FRPs). However, it is hard for VPPs to make accurate bidding in the market, due to the uncertainty of their dispatching capability and system requirements. Therefore, a cloud-edge collaborated market architecture supporting VPPs participation in the electricity market and providing FRPs services is proposed, and the corresponding distributed optimization trading model is established. The market clearing process is completed through the collaborative interaction between the independent system operator and VPPs, which can accurately guide VPPs to optimize the electricity consumption and provide flexible climbing services. The distributed optimization model is iteratively solved using the analytical target cascading (ATC) method, and heuristic constraints are introduced to improve the convergence of the algorithm. Finally, the proposed method is evaluated by the simulation results of typical cases featuring the “duck-curve” net load, which demonstrate that the cloud-edge collaborated market can effectively reduce operating costs and promote the consumption of renewable energy.

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    Frequency-Domain Modeling and Synchronization Perspective Interaction Mechanism of GFL-GFM Converter System
    ZONG Haoxiang, ZHANG Chen, BAO Yanhong, WU Feng, CAI Xu
    Journal of Shanghai Jiao Tong University    2025, 59 (2): 151-164.   DOI: 10.16183/j.cnki.jsjtu.2023.231
    Abstract893)   HTML23)    PDF(pc) (4555KB)(1255)       Save

    Aimed at the small-signal synchronization instability of grid-following (GFL) and grid-forming (GFM) converter system, a synchronization perspective frequency-domain modeling and analysis method is proposed, which can intuitively reveal mechanism and accurately judge multi-machine stability. Specifically, a node admittance matrix considering GFL, GFM converters, and the transmission network is established. Then, the frequency domain modal analysis (FMA) method is adopted to evaluate system instability characteristics. Afterwards, synchronization forward and feedback paths are partitioned at the oscillation source to formulate a synchronization perspective stability model incorporating dynamics of each converter and transmission network. Finally, the proposed method is validated by using a typical two-machine GFL-GFM system. With such method, the stability judgment failure caused by the feedback path aggregation is addressed, and the interaction mechanism between GFL and GFM synchronization dynamics as well as their parameter influences are revealed.

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    Interpretation of Chinese Guidelines for the Prevention and Management of Bronchial Asthma (2024 Edition)
    ZHOU Yan, ZHANG Min
    Journal of Diagnostics Concepts & Practice    2025, 24 (04): 415-422.   DOI: 10.16150/j.1671-2870.2025.04.008
    Abstract1525)   HTML43)    PDF(pc) (509KB)(1232)       Save

    According to the Global Burden of Disease (GBD) data for 2021, the global age-standardized prevalence of asthma is 3 340.1/100 000, with a total of about 260 million patients, a mortality rate of 5.2/100 000, and 436 000 deaths. A 2012-2015 survey conducted in China shows that the prevalence of wheezing-related asthma among people aged 20 and above is 4.2%, with a total of about 45.7 million patients. However, the diagnosis rate is only 28.8%, and the control rate is only 28.5%, far below the international level, highlighting the urgent need for better asthma management and intervention. In March 2024, the Chinese Thoracic Society (CTS) released the Guidelines for the Prevention and Management of Bronchial Asthma (2024 Edition) (hereinafter referred to as the "2024 Guidelines"). For diagnostic pathways, the 2024 Guidelines improve the diagnostic criteria for asthma, emphasizing the evidence for variable expiratory airflow (such as bronchodilator tests, provocation tests, etc.). A "presumptive diagnosis pathway" is proposed for primary care and resource-limited medical institutions to improve the diagnosis rate and avoid overtreatment. In terms of staging and classification, the concept of "clinical remission" is introduced, defined as being asymptomatic for ≥1 year without the need for systemic glucocorticoid therapy. The classification of "intermittent state" is eliminated, and asthma severity is now simplified into three levels—mild, moderate and severe—with a dynamic assessment model proposed. The assessment system newly includes a type 2 inflammatory phenotype assessment, recommending the measurement of biomarkers such as peripheral blood eosinophil count (EOS) and fractional exhaled nitric oxide (FeNO) to guide individualized treatment, while also emphasizing comorbidity screening and risk factor assessment. In terms of treatment strategies, a stepwise management approach is used for chronic persistent treatment, with inhaled corticosteroid (ICS)-formoterol recommended as the preferred reliever (Pathway 1) to reduce the risk of acute exacerbations. The management of severe asthma emphasizes the use of biological targeted drugs, such as anti-IgE and anti-interleukin (IL)-5 monoclonal antibodies, while the treatment of acute exacerbations is recommended based on the severity level. Despite the significant progress made in the 2024 Guidelines, challenges remain. Epidemiological data on asthma in China are outdated, highlighting the urgent need for nationwide surveys to reflect the latest disease burden. Diagnosis rates in primary care are low, and inflammation assessment and dynamic mana-gement are insufficient, requiring strengthened capacity building at the primary care level. Real-world data on biologics in China are limited, restricting their application in precision therapy. The application of information technology in asthma management is still at an exploratory stage, and technologies like 5G should be leveraged to enhance patient education and follow-up efficiency. In the future, asthma prevention and treatment in China need to further optimize strategies for early diagnosis and early treatment, dynamically identify inflammatory phenotypes, establish drug response prediction models, and promote AI-assisted diagnosis and treatment to achieve more precise management.

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    Coordinated Day-Ahead Scheduling and Real-Time Dispatch of a Wind-Thermal-Storage Energy Base Considering Flexibility Interval
    YANG Yinguo, FENG Yinying, WEI Wei, XIE Pingping, CHEN Yue
    Journal of Shanghai Jiao Tong University    2025, 59 (9): 1270-1280.   DOI: 10.16183/j.cnki.jsjtu.2023.509
    Abstract1925)   HTML4)    PDF(pc) (1483KB)(1223)       Save

    Large-scale new energy bases in desert, Gobi, and arid regions are key components of new-type power systems in China. Considering factors such as construction cost and carbon emissions, the capacities of thermal power and energy storage in these bases are limited, resulting in constrained flexibility. Consequently, the scheduling and operation of these large bases face significant challenges. This paper proposes a coordinated day-ahead and real-time scheduling method for wind-thermal-storage integrated bases. In the day-ahead stage, the startup/shutdown plans and adjustable output ranges of thermal units are determined based on a rough prediction of wind power. Then, it constructs a wind power accommodation interval based on the adjustable range of thermal power output and the operational constraints of energy storage. In the real-time stage, dispatch strategies are generated using a quantile-based rule according to current wind and solar power output, eliminating the need for high-precision forecasts. It is further demonstrated that the dispatch strategies generated by the quantile rule inherently satisfy system operational constraints. The case study validates the effectiveness of the proposed method for wind-thermal-storage systems. The results demonstrate that the proposed method, which does not rely on point prediction, outperforms rolling optimization methods when the three-step prediction error exceeds 10%. Moreover, the performance of operational scheduling can be improved by enhancing the accuracy of day-ahead or intraday short-term forecasts. The proposed method provides valuable reference for the operation of large-scale new energy bases.

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    Fast Prediction for Roll Motion of a Damaged Ship Based on SVR
    LIU Han, SU Yan, ZHANG Guoqiang
    Journal of Shanghai Jiao Tong University    2025, 59 (7): 1041-1049.   DOI: 10.16183/j.cnki.jsjtu.2023.431
    Abstract340)   HTML2)    PDF(pc) (5899KB)(1183)       Save

    ANSYS-AQWA is applied to analyze the rolling motion response of the damaged ship DTMB5415 under various working conditions. The results are compared with those in exiting literature to validate the practicality of the hydrodynamic model. Additionly, the rolling motion response database for the damaged ship is constructed. The support vector regression (SVR) algorithm is used to model the rolling motion database for identification, exploring the relationship between the operating condition factors and coefficients in the equation of roll motion. Finally, a fast prediction model for rolling motion is constructed and validated, offering a significant improvement in the prediction efficiency compared with traditional computational fluid dynamics models.

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    Interpretation of key points in 2025 KDIGO Clinical Practice Guideline for the Evaluation,Management,and Treatment of Autosomal Dominant Polycystic Kidney Disease
    WU Shuangcheng, YU Shengqiang
    Journal of Diagnostics Concepts & Practice    2025, 24 (03): 255-262.   DOI: 10.16150/j.1671-2870.2025.03.003
    Abstract607)   HTML18)    PDF(pc) (1343KB)(1162)       Save

    Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common hereditary renal cystic disorders and a major cause of end-stage renal disease requiring renal replacement therapy. In February 2025, Kidney Disease: Improving Global Outcomes (KDIGO) released the first clinical practice guideline specifically for ADPKD entitled "KDIGO Clinical Practice Guideline for the Evaluation, Management, and Treatment of Autosomal Dominant Polycystic Kidney Disease". The guideline comprises 10 chapters covering nomenclature, diagnosis, prognosis, and prevalence of ADPKD; renal manifestations; management and progression of chronic kidney disease, renal failure, and renal replacement therapy; treatments to delay renal disease progression; polycystic liver disease; intracranial aneurysms and other extrarenal manifestations; lifestyle and psychosocial considerations; pregnancy and reproductive problems; pediatric problems; and approaches to ADPKD patient management. It highlights early diagnosis, risk stratification, integrated management, and application of the new drug tolvaptan. Additionally, the guideline introduces a new nomenclature system based on pathogenic genes for the first time, along with more stringent blood pressure management plans. By integrating guideline highlights, evidence-based medicine, and China's clinical practice, this study interprets two key clinical issues in the updated guideline: "early diagnosis and risk stratification of ADPKD" and "treatment and daily management of kidney-related symptoms." A thorough analysis of the guideline's implications and limitations is conducted, aiming to promote standardized diagnosis and therapy for ADPKD.

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    Oscillatory Stability Assessment of Renewable Power Systems Based on Frequency-Domain Modal Analysis
    GAO Lei, MA Junchao, LÜ Jing, LIU Jianing, WANG Chenxu, CAI Xu
    Journal of Shanghai Jiao Tong University    2025, 59 (6): 821-835.   DOI: 10.16183/j.cnki.jsjtu.2023.358
    Abstract2066)   HTML8)    PDF(pc) (4521KB)(1146)       Save

    The increasing penetration of the renewable energy has increased the risks of sub/super synchronous oscillations in power systems. Therefore, it is critical to accurately evaluate the oscillatory stability of renewable power systems ensuring the safe and stable operation of the systems. In this paper, a method for evaluating the oscillatory stability of renewable power systems based on frequency-domain modal analysis is investigated. First, the frequency-domain impedance or admittance models of key equipment and stations are established, including the renewable power generators and stations, transmission lines, synchronous generators, transformers, etc. Next, a system-level frequency-domain network model is constructed based on the actual system topology. Then, the oscillatory stability of the renewable power system is evaluated by solving the zeros of the determinant of the loop impedance matrix or the node admittance matrix of the system. The weak points of the system are identified using the participated matrix of the weak oscillation mode, which provides reference for implementation of oscillation suppression measures. Taking the practical renewable power system in East China as an example, the oscillatory stability of the system considering the varying access capacity of renewables under different grid operating conditions is assessed using the frequency-domain modal analysis method. Finally, the time-domain simulation model of the actual renewable power system is built in PSCAD/EMTDC to verify the theoretical analysis.

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    Exploring Pathways for Integrating International Communication Competence and Foreign Language Talent Development
    YANG Lianrui, ZHANG Hui, LIU Xiaolan
    Contemporary Foreign Languages Studies    2025, 25 (1): 34-42.   DOI: 10.3969/j.issn.1674-8921.2025.01.004
    Abstract649)   HTML11)    PDF(pc) (1267KB)(1144)       Save

    The capacity for international communication is a critical indicator of a nation’s soft power, with the cultivation of high-caliber talent serving as the cornerstone of this development. Mastery of foreign languages is indispensable for enhancing international communication, and foreign language majors should aim to produce versatile professionals equipped with a strong sense of national identity, global awareness, and specialized expertise. This paper, grounded in the framework of the New Liberal Arts, proposes the establishment of an innovative talent cultivation platform within foreign language majors to enhance educational efficacy and better support the development of international communication competencies. The platform seeks to integrate resources across institutions and classrooms, providing interdisciplinary and cross-professional knowledge and skills. Its primary focus is on cultivating students’ abilities in critical areas such as comprehension, interpretation, analytical thinking, and innovation. Meanwhile, the platform is distinguished by four core features: broad scope, targeted relevance, adaptability, and a strong emphasis on practical application. It offers foreign language students an innovative environment to reinforce their foundational knowledge while enhancing their capacity to contribute to global communication efforts.

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    Design Methods for Power Secondary System Simulation in New Power Systems
    HE Ruiwen, LU Jialiang, YANG Changxin, PENG Hao, MOHAMMAD Shahidehpour
    Journal of Shanghai Jiao Tong University    2025, 59 (10): 1419-1430.   DOI: 10.16183/j.cnki.jsjtu.2023.541
    Abstract1743)   HTML11)    PDF(pc) (3327KB)(1139)       Save

    Under the new situation, there is an urgent need to model and simulate the power secondary system which highly shares information and implements real-time decision-making, in line with the modeling and simulation requirements of new power systems. In this paper, design methods are proposed for the first time to achieve simulation of power secondary systems by correlating the operating status of the power primary system. The smart substation secondary system with complex functional descriptions is taken as the research object. First, an interrelated simulation method for power primary and secondary systems is proposed, and its simulation implementation framework, data interaction method, and data synchronization management are explained, which enables the actual electrical quantity data of the primary system to be transmitted to the secondary side, solving the problem of data source in the secondary system simulation. Then, a simulation design method for the power secondary system is proposed, incorporating system-level interaction design, component-level class design, and module-level state design based on the object-oriented unified modeling language (UML). Thus, the entire process of transmission, interaction, processing, and conversion of electrical quantity data in the secondary system can be analyzed. Finally, to validate the effectiveness of the proposed method, a case study is conducted using a short-circuit fault scenario at the 110 kV side outlet of the 220/110/10 kV main transformer bay, in conjunction with a differential protection scheme.

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    From Kill Chain to Kill Web: A Survey on Modeling, Evaluation, and Optimization
    WANG Chaochen, JIANG Hongru, WANG Buli, XIA Qiaowei, ZHANG Xianchun
    Air & Space Defense    2025, 8 (4): 1-8.  
    Abstract2715)      PDF(pc) (992KB)(1117)       Save
    This paper comprehensively and systematically analyzed the theoretical evolution, model construction, effectiveness evaluation, and optimization methodologies of kill chains and webs. First, the fundamental distinctions between kill chains and kill webs were introduced via conceptual comparative analysis. Then, from the perspective of four key modeling challenges: structured information representation, cooperative system optimization, dynamic adaptability, and intelligent decision-making, the construction mechanisms and technological breakthroughs of various models were investigated. Quantitative evaluation methods for key dimensions, including survivability, resilience, and node importance, were summarized. Following this, strategies for dynamic reconstruction optimization and multi-objective conflict resolution were studied. Finally, future development trends of kill chains and kill webs were projected.
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    Physics-Informed Fast Transient Stability Assessment of Non-Fixed Length in Power Systems
    LI Xiang, CHEN Siyuan, ZHANG Jun, KE Deping, GAO Jiemai, YANG Huanhuan
    Journal of Shanghai Jiao Tong University    2025, 59 (7): 962-970.   DOI: 10.16183/j.cnki.jsjtu.2023.452
    Abstract2096)   HTML4)    PDF(pc) (1706KB)(1105)       Save

    Against the backdrop of “dual carbon” goals, the construction of a new power system with new energy as the main component is the main direction and key way for the transformation and upgrading of the power industry. Research into fast and accurate evaluation of transient power angle stability in the context of new power systems is of great significance. To address this, a new transient power angle stability evaluation method is proposed for power systems with grid-forming new energy based on the physics-informed sequence-to-sequence (PI-seq2seq) neural networks and cascaded convolutional neural networks models. First, the PI-seq2seq network structure is used to predict the future power angle trajectory, and a loss function with physical loss terms is constructed to guide the model training process, which avoids the long-duration time-domain simulation to ensure fast transient evaluation. Then, predicted power angle trajectory is taken as input by the cascade convolutional neural networks to evaluate the transient stability and its confidence level. A threshold judgment mechanism for the evaluation confidence level is configured to realize the transient stability judgment of the non-fixed evaluation length, which overcomes the impact of the fixed power angle curve length on the evaluation results. Finally, the method proposed is verified in the Kundur system, and the simulation results show that it has obtained satisfactory results in both the power angle curve prediction and the stability evaluation.

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    Precise Foot Feature Point Localization and Automatic Parameters Measurement
    JI Mian, LIN Yanping, WANG Dongmei, CHEN Li, MA Xin
    Journal of Shanghai Jiao Tong University    2025, 59 (5): 703-710.   DOI: 10.16183/j.cnki.jsjtu.2023.397
    Abstract1279)   HTML4)    PDF(pc) (11865KB)(1093)       Save

    In order to quickly obtain foot parameters and quantify the degree of foot deformation, an algorithm that can accurately locate foot feature points and automatically calculate foot parameters is proposed. First, a total of 93 patients participate and their foot models are obtained using the UPOD laser scanner. Then, the random sampling consensus algorithm and principal component analysis are used to align the foot coordinate system. The algorithm utilizes foot features to identify and locate feature points, enabling the parameter calculation of length, angle, and girth. The accuracy, repeatability, and consistency of the measurements are evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), interclass correlation coefficient (ICC), and Bland-Altman plots. The MAE of foot length and width is less than 2 mm, and for ball girth, instep girth, and heel girth, it is less than 4 mm. The MAPE is less than 2%, and the ICCs for the three replicates exceed 0.99. More than 95% of the scattered points in the Bland-Altman plots are within the consistency boundary. The results show that the proposed algorithm can automatically align the coordinate system, accurately locate feature points, and accurately measure foot parameters in the standing posture. The measurement accuracy meets clinical needs with high accuracy and reliability. The findings provide valuable data support for foot classification, intelligent assistive device adaptation, and personalized assistive device design, showing important clinical application potential.

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    A Multiscale Simulation of Surface Discharge and Discharge Signals in SF6
    ZHOU Lubo, ZHANG Zhaoqi, WANG Dong, SONG Hui
    Journal of Shanghai Jiao Tong University    2025, 59 (9): 1397-1406.   DOI: 10.16183/j.cnki.jsjtu.2023.525
    Abstract1110)   HTML7)    PDF(pc) (7214KB)(1072)       Save

    Surface discharge is a common type of discharge occurring in gas-insulated switchgear equipment, of which the microscopic process remains unclear. Additionly, there is a lack of theoretical correlation between the microscopic process of partial discharge due to defects and the macroscopic detection signals. First, the surface discharge process in SF6 is simulated based on a fluid-chemical simulation model, revealing the variation patterns of charged particle concentration and surface streamer velocity. Then, taking the current pulse generated in the microscopic discharges as excitation sources, the discharge signals resulting from the surface discharges are simulated based on the finite integral method, establishing a correspondance between the microscopic partial discharge process and the detectable discharge signals. Compared with the conventional Gaussian excitation source, the time-domain waveforms of electromagnetic signals obtained from the microscopic discharge simulation more chosely matches to realistic conditions. These findings effectively supplement existing researches on the microscopic mechanisms of partial discharge signals, laying a foundation for the insulation state evaluation based on the discharge signal analysis.

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    Influence of Sea Condition on Tracking Accuracy and Control Energy Consumption of Unmanned Surface Vessels under Straight Line Navigation
    YONG Jiuqin1, 2, XUE Zhentao1, 2, CHEN Li1, 2
    Ocean Engineering Equipment and Technology    2024, 11 (4): 103-109.   DOI: 10.12087/oeet.2095-7297.2024.04.16
    Abstract443)      PDF(pc) (2226KB)(1070)       Save
    Adaptability to various sea conditions is vital to unmanned surface vessels (USVs). This paper aims to reveal influence of sea conditions on tracking performance. The USV controlled by PID tuned for the baseline sea condition is studied. A three degree of freedom dynamic model is built, and a new evaluation index of control energy consumption accounting for propeller propulsion and steering mechanism operation is designed other than the conventional tracking accuracy. Simulation results demonstrate the variation trend of the tracking accuracy and control energy consumption with sea conditions under the typical straight line navigation case. The performance is significantly affected under high sea conditions, with root mean square error increasing 188 times up to 158.207m and control energy consumption increasing 3 times up to 46.916 kWh. The quantification of the tracking accuracy and control energy consumption with sea conditions provides reference for the design and control of USVs.
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    Unit Commitment Optimization Model Considering Impact of Multiple Operating Conditions on Unit Life Loss
    LUO Yifu, HU Qinran, QIAN Tao, CHEN Tao, ZHANG Yuanshi, ZHANG Fei, WANG Qi
    Journal of Shanghai Jiao Tong University    2025, 59 (6): 768-779.   DOI: 10.16183/j.cnki.jsjtu.2023.401
    Abstract2121)   HTML9)    PDF(pc) (3746KB)(1038)       Save

    Thermal power units face a dilemma of accelerated lifespan degradation and extended service duration. On one hand, large-scale integration of new energy sources has increased peak shaving conditions and accelerated losses of the units. On the other hand, service units will reach designed lifespan before carbon neutrality is achieved, while flexible operation of the power system necessitates extending their service life of units. Therefore, it is of great significance to consider the losses caused by varicus operating conditions on the lifespan of the unit and optimize the operating structure of the unit in scheduling simulation for unit longevity and carbon reduction efforts. To make unit life losses in theoretical research more practical, the traditional model that averages the losses in deep peak shaving conditions has been discarded. Instead, new judgment criteria for conventional and various special operating conditions of thermal power units are established. The lifespan loss cost of the unit is integrated into the operating objective function and the corresponding constraint conditions are modified. Finally, a unit commitment model considering the multi-operating condition lifespan losses of thermal power units is constructed. Example simulations indicate that the conventional model underestimates the actual loss cost of the units. In constrast, the proposed model can not only reduce the operating cost and unit life loss by considering the lifespan impacts of multi-operating conditions, but also enhance the peak shaving capacity of thermal power units and promote wind power consumption.

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    Research on Construction Method of Digital-Intelligent Parallel Battlefield for Air Defense and Anti-Missile Based on Digital Twin
    WANG Gang, YANG Ke, QUAN Wen, GUO Xiangke, ZHAO Xiaoru
    Air & Space Defense    2025, 8 (3): 1-13.  
    Abstract2570)      PDF(pc) (1209KB)(1007)       Save
    The establishment of a digital parallel battlefield that integrates the functions of "research, combat, testing, practical operation, and training" serves as an effective and essential supporting measure for enhancing combat command and joint training capabilities, which address future intelligent high-end warfare among major powers. In the air defense and anti-missile realm, constructing a digital parallel battlefield faces several formidable challenges, including configuring complex scenarios, facilitating interactions between virtual and real forces, and simulating combat behaviours. To tackle these issues, the paper employed a digital twin modelling approach grounded in Model-Based Systems Engineering (MBSE) to enable the precise configuration of complex scenarios. Based on the Live-Virtual-Constructive (LVC) concept, a distributed simulation architecture combining virtual and real elements was established to realise seamless virtual-real interactions and efficient coordination. In addition, an Agent modelling methodology based on data/rules dual-driven was introduced to simulate intelligent combat behaviours in air defense and anti-missile operations. A multi-branch simulation deduction and auxiliary decision-making framework tailored for the parallel battlefield was constructed, achieving the organic integration of situation analysis, plan formulation, and evaluation for optimal selection. The system development was accomplished by applying the software-defined method, enabling dynamic scheduling of system resources, flexible reconfiguration, and agile deployment. The research results indicate that the newly developed digital-intelligent parallel battlefield for air defense and anti-missile, constructed by the concept of the digital twin, provides robust support for simulation applications across multiple scenarios, including equipment testing, combat experiments, joint training, and command decision-making within the domain of air defense and anti-missile.
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    Multi-Objective Optimization Design of Ship Propulsion Shafting Based on Response Surface Methodology and Genetic Algorithm
    ZHANG Cong, SHU Bingnan, ZHANG Jiangtao, JIN Yong
    Journal of Shanghai Jiao Tong University    2025, 59 (4): 466-475.   DOI: 10.16183/j.cnki.jsjtu.2023.318
    Abstract1950)   HTML12)    PDF(pc) (9630KB)(988)       Save

    In order to reduce the power loss of the transmission equipment, enhance the transmission efficiency of the propulsion shafting, and improve the vibration performance of the shafting, a multi-objective optimization design of a ship shafting experimental platform is performed based on the response surface model and genetic algorithm. The central composite design (CCD) method is used to select appropriate experimental points in the optimized design space, and the response surface model is developed with minimum total power consumption and vibration response amplitude. Based on the genetic algorithm, the Pareto optimal solution of response surface model regression function is solved through MATLAB software. The optimal design scheme is obtained by comparing and analyzing several groups of optimization results. The results show that the combined method can reduce the power loss of shafting by approximate 7.10% and reduce the vibration amplitude of shafting by 2.30%, while significantly improving the shafting transmission efficiency and effectively suppressing the vibration problem of propulsion shafting. The fiudings validate the feasibility of the multi-objective optimization method for the ship propulsion shafting.

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